1. Technical Field
This disclosure relates to a system and method for tracking and locating a person, animal, or machine.
2. Description of Related Art
A basic principle in navigation processes is known as “dead reckoning.” In dead reckoning, the current position of an object is estimated by measuring the course, speed, and time elapsed since the object was in a known starting position. For example, a person who is orienteering may utilize dead reckoning. By multiplying their average walking speed by the elapsed time, the person can estimate a total distance travelled. Using a map, the person can thus plot their path from a known starting position along a measured compass course. Dead reckoning, however, is only as reliable as the data that is used. In the orienteering example, error may be introduced in the estimates of the course travelled, the average speed, and the elapsed time. As technology developed, inertial navigation systems were developed to address these sources of error to enable more accurate navigation.
Dead reckoning devices may be used by first responders and warfighters to track the location of the first responder or warfighter when tracking systems, such as Global Positions Systems (GPS), are unavailable. Instances where GPS is unavailable may include underground or deep natural locations, such as mines, canyons, caves, tunnels, bunkers, and basements; urban locations, such as skyscrapers and other large buildings; or locations with active interference of GPS signals or high levels of electromagnetic interference.
Inertial navigation systems perform a sensor-based dead reckoning by using data from an inertial measurement unit (IMU) separately or in combination with a digital magnetic compass (DMC). IMUs typically include three mutually orthogonal linear accelerometers and three mutually orthogonal rate gyroscopes to collect time-series data of linear accelerations and angular rates in the reference frame of the IMU. Generally the three accelerations are measured by accelerometers and the angular rates are measured by gyroscopes. These six values fully characterize the dynamics of the IMU. DMCs provide three measurements of the strength of the measured magnetic field relative to the same fixed orthogonal axes used to calculate linear acceleration. In the absence of magnetic disturbances, the direction of the measured magnetic field vector gives an estimate of the “magnetic heading.”
There are various grades of IMU, which vary by cost, accuracy, size, and weight. The heaviest, most expensive, and most accurate are “navigation-grade” IMUs, which often weigh between five and one hundred pounds. The weight and cost of such IMUs make them impractical for most human-portable applications. At the other end of the spectrum are “industrial-grade” IMUs, which are considerably lighter and less expensive, making them more practical for human-portable applications despite the decreased accuracy. In the middle of the spectrum are “tactical-grade” IMUs.
Typical IMUs and DMCs provide data at roughly 50 to 1000 samples per second. The “operating frequency” of, for example, an IMU is defined as the number of times per second that the six measurements (three linear accelerations and three angular rates) are taken. Each of these time steps is known as “data cycles.” During each data cycle, the six measurements, collectively known as an “IMU data packet,” are sent to a data processor in the inertial navigation system to integrate navigation equations known in the art to produce a current estimate of the velocity. The data processor then integrates again to produce current estimates of the position and the attitude (i.e. pitch, roll and navigation heading) of the IMU.
In a similar manner, the three measurements of magnetic field strength, collectively known as a “DMC data packet” are also taken and sent to the data processor in the inertial navigation system to produce a current estimate of the magnetic heading. The operating frequency of the IMU and the DMC may be the same or different.
The integrations performed by the data processor inherently produce finite errors in the real-time estimates of the position, velocity, and attitude, collectively known as “navigation errors.” If uncorrected, these errors grow unbounded with time. To help bound the navigation errors, it is common in the art to employ various filtering techniques. One class of filters often used in inertial navigation systems is known as Kalman filters. The term “Kalman filter” will be used to collectively refer to members and variants in this class of filters, including but not limited to extended Kalman filters (EKF) and unscented Kalman filters (UKF). Kalman filters are further described in U.S. Pat. No. 8,224,575 (which is assigned to the same assignee as the present patent application), the disclosure of which is hereby incorporated by reference in its entirety.
Although navigation errors are significantly decreased using Kalman filters, other sources of error, such as inaccuracies inherent to an IMU, persist. Because the position estimate is often the most important output of an inertial navigation system, one goal of such a system is to limit the time-induced increase of errors in the position estimate. For simplification, this specification will subdivide position errors into two components, “along-track” and “cross-track” errors. “Along-track” errors refer to errors in the total distance traveled while “cross-track” errors refer to errors in the direction perpendicular to the direction of travel. Cross-track and along-track errors may also be referred to collectively as “heading errors” or “navigation errors.”
Several techniques have been utilized to help reduce navigation errors in a human-portable inertial navigation system in order to make such systems more accurate for a longer period of use. One such technique for constraining both along-track and cross-track errors is to implement a zero-velocity update (ZUPT) when the IMU is at rest. During a ZUPT, the IMU updates the Kalman fitter with a velocity vector showing that the IMU is at zero velocity. Based on a ZUPT, the system may correct errors in the attitude by using the vertical gravity vector, correct position errors, and estimate correlated IMU errors. Generally, the accuracy of the real-time position estimate from the Kalman filter is directly related to the frequency of the ZUPTs.
ZUPTs are often implemented by placing an IMU on the shoe or boot of a user to utilize the fact that a shoe is normally at zero velocity for a period of time, however brief, during part of a movement, regardless of whether the user is running, walking, or even crawling. This stationary period is exploited to constrain navigation and position errors.
Current ZUPT methods may detect zero-velocity conditions for users who are walking or moving at relatively slow velocities. However, during fast movements, such as jogging, running, or sprinting, current ZUPT methods are inadequate for recognizing zero-velocity conditions. For example, during fast movements, the rotation of IMU may continue through the movement, although the overall velocity of the IMU is close to zero. Similarly, during fast movements, the IMU may be rotated such that gravity adds an additional component to each IMU axis, masking the actual acceleration components of IMU.
In addition to implementing ZUPTs, some systems use global positioning system (GPS) satellites to update the Kalman filter with a known position, thereby constraining position errors. These systems utilize a single GPS receiver, often mounted on the shoulder or torso of the user. One limitation with the use of GPS, however, is that in many locations, such as underground or in a building away from windows, a user may not be able to receive data from GPS satellites.
In the absence of a GPS signal, pedestrian tracking and navigation systems, such as inertial navigation systems, often determine a location based on the relative motion of the user, for example using an IMU located in a single shoe, boot, or other footwear of the wearer. However, these single-boot systems usually only provide acceptable tracking and position information for short periods of time, typically 15 to 30 minutes. A theoretical foot-to-foot personal tracking system was proposed by Brand and Phillips, in Foot-to-Foot Range Measurement as an Aid to Personal Navigation, the disclosure of which is hereby incorporated by reference in its entirety. Brand and Phillips proposed a continuous foot-to-foot measurement system to determine the distance between each of a user's feet, thereby tracking the location of the pedestrian. Laverne et al. attempted to apply the Brand and Phillips system, reporting their results in Experimental Validation of Foot to Foot Range Measurements in Pedestrian Tracking, the disclosure of which is hereby incorporated by reference in its entirety. However, Brand and Phillips, and Laverne et al., suffer from several flaws, including inadequate RF-ranging in the foot-to-foot measurement, inadequate IMU mounting, lack of an integrated GPS receiver into the system, and inadequate filter updates to constrain navigation errors. As such, personal navigation systems proposed by Brand and Phillips and Laverne et al. remain unacceptable to use during long durations of time.
Some dead reckoning systems include RF ranging systems that may utilize a round-trip time-of-flight measurement to compute the distance between two radios, where one radio is mounted on each shoe or boot of the user. These types of systems can be further classified into “round-trip full-duplex” configurations and “round-trip half-duplex” configurations. In a round-trip full-duplex configuration, a first radio, such as an antenna, transmits a signal to a second radio, which then retransmits the same signal back to the first radio without performing any calculations using the signal. After receiving the retransmitted signal from the second radio, the first radio compares the departure time to the arrival time to calculate the round-trip signal propagation time. The system multiplies this time by the speed of light and divides by two to estimate the distance between the two radios.
In a round-trip half-duplex configuration, a first radio transmits a signal to a second radio, which then performs calculations using that signal. The second radio then transmits a new signal, which often contains the results of the calculations performed by the second radio, back to the first radio. The first radio then utilizes the data from the second radio and other data within the first radio to calculate the round-trip signal propagation time. The system multiplies this time by the speed of light and divides by two to estimate the distance between the two radios.
An improved RF-ranging technique is disclosed in U.S. Pat. No. 8,199,047 (which is assigned to the same assignee as the present patent application), the disclosure of which is hereby incorporated by reference in its entirety.
As used herein, an “antenna” refers to a device that emits, transmits and/or receives wireless signals. Such devices may be generally referred to as “emitters,” “transmitters,” “receivers,” or “transceivers” in the art and may emit, transmit, and/or receive electromagnetic signals (such as RF signals), ultrasonic signals, subsonic signals, optical signals, acoustic signals, or other signals through the air. Such devices may be metallic or non-metallic and may include emitting elements, transmitting elements, receiving elements, and/or transceiving elements, depending on the type of signal being emitted, transmitted or received.
Although there have been some improvements in tracking technology, there remains a need for an improved portable tracking and location system that obviates or at least mitigates one or more of the shortcomings of previous techniques to allow more accurate computation of the current or real-time position of a user in a wide range of operating environments for an extended period of time.